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Found 3 papers, 2 papers with code
On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data.
MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In(Variant) Representations
Contrastive self-supervised learning has gained attention for its ability to create high-quality representations from large unlabelled data sets.
Ranked #1 on
Few-Shot Audio Classification
on Common Voice
(using extra training data)
MetaAudio: A Few-Shot Audio Classification Benchmark
Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification.
Ranked #1 on
Few-Shot Audio Classification
on NSynth
